Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner

Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):933-941, 2013.

Abstract

For a well trained Boosting classifier, we are interested in how to save the testing time, i.e., to make the decision without evaluating all the base learners. To address this problem, in previous work the base learners are sequentially calculated and early stopping is allowed if the decision function has been confident enough to output its value. In such a chain structure, the order of base learners is critical: better order can lead to less evaluation time. In this paper, we present a novel method for ordering. We base our discussion on the data structure representing Boosting’s decision function. Viewing the decision function a boolean expression, we propose a Binary Valued Tree for its representation. As a secondary contribution, such a representation unifies the work by previous researchers and helps devise new representation. Also, its connection to Binary Decision Diagram(BDD) is discussed.

Related Material

@InProceedings{pmlr-v28-sun13,
title = {Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner},
author = {Peng Sun and Jie Zhou},
booktitle = {Proceedings of the 30th International Conference on Machine Learning},
pages = {933--941},
year = {2013},
editor = {Sanjoy Dasgupta and David McAllester},
volume = {28},
number = {3},
series = {Proceedings of Machine Learning Research},
address = {Atlanta, Georgia, USA},
month = {17--19 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v28/sun13.pdf},
url = {http://proceedings.mlr.press/v28/sun13.html},
abstract = {For a well trained Boosting classifier, we are interested in how to save the testing time, i.e., to make the decision without evaluating all the base learners. To address this problem, in previous work the base learners are sequentially calculated and early stopping is allowed if the decision function has been confident enough to output its value. In such a chain structure, the order of base learners is critical: better order can lead to less evaluation time. In this paper, we present a novel method for ordering. We base our discussion on the data structure representing Boosting’s decision function. Viewing the decision function a boolean expression, we propose a Binary Valued Tree for its representation. As a secondary contribution, such a representation unifies the work by previous researchers and helps devise new representation. Also, its connection to Binary Decision Diagram(BDD) is discussed.}
}

%0 Conference Paper
%T Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner
%A Peng Sun
%A Jie Zhou
%B Proceedings of the 30th International Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2013
%E Sanjoy Dasgupta
%E David McAllester
%F pmlr-v28-sun13
%I PMLR
%J Proceedings of Machine Learning Research
%P 933--941
%U http://proceedings.mlr.press
%V 28
%N 3
%W PMLR
%X For a well trained Boosting classifier, we are interested in how to save the testing time, i.e., to make the decision without evaluating all the base learners. To address this problem, in previous work the base learners are sequentially calculated and early stopping is allowed if the decision function has been confident enough to output its value. In such a chain structure, the order of base learners is critical: better order can lead to less evaluation time. In this paper, we present a novel method for ordering. We base our discussion on the data structure representing Boosting’s decision function. Viewing the decision function a boolean expression, we propose a Binary Valued Tree for its representation. As a secondary contribution, such a representation unifies the work by previous researchers and helps devise new representation. Also, its connection to Binary Decision Diagram(BDD) is discussed.

TY - CPAPER
TI - Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner
AU - Peng Sun
AU - Jie Zhou
BT - Proceedings of the 30th International Conference on Machine Learning
PY - 2013/02/13
DA - 2013/02/13
ED - Sanjoy Dasgupta
ED - David McAllester
ID - pmlr-v28-sun13
PB - PMLR
SP - 933
DP - PMLR
EP - 941
L1 - http://proceedings.mlr.press/v28/sun13.pdf
UR - http://proceedings.mlr.press/v28/sun13.html
AB - For a well trained Boosting classifier, we are interested in how to save the testing time, i.e., to make the decision without evaluating all the base learners. To address this problem, in previous work the base learners are sequentially calculated and early stopping is allowed if the decision function has been confident enough to output its value. In such a chain structure, the order of base learners is critical: better order can lead to less evaluation time. In this paper, we present a novel method for ordering. We base our discussion on the data structure representing Boosting’s decision function. Viewing the decision function a boolean expression, we propose a Binary Valued Tree for its representation. As a secondary contribution, such a representation unifies the work by previous researchers and helps devise new representation. Also, its connection to Binary Decision Diagram(BDD) is discussed.
ER -

Sun, P. & Zhou, J.. (2013). Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner. Proceedings of the 30th International Conference on Machine Learning, in PMLR 28(3):933-941